Abstract
Although spatial smoothing of fMRI data can serve multiple purposes, increasing the sensitivity of activation detection is probably its greatest benefit. However, this increased detection power comes with a loss of specificity when non-adaptive smoothing (i.e. the standard in most software packages) is used. Simulation studies and analysis of experimental data was performed using the R packages neuRosim and fmri. In these studies, we systematically investigated the effect of spatial smoothing on the power and number of false positives in two particular cases that are often encountered in fMRI research: (1) Single condition activation detection for regions that differ in size, and (2) multiple condition activation detection for neighbouring regions. Our results demonstrate that adaptive smoothing is superior in both cases because less false positives are introduced by the spatial smoothing process compared to standard Gaussian smoothing or FDR inference of unsmoothed data.
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Notes
We explicitly discard cluster-based FDR methods (e.g. Chumbley and Friston 2009) because they are also dependent on RFT inference.
The influence of spatial smoothing in this stage was minimized by setting the FWHM of the Gaussian smoothing kernel to 1 mm, before estimating the realignment parameters.
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Welvaert, M., Tabelow, K., Seurinck, R. et al. Adaptive Smoothing as Inference Strategy. Neuroinform 11, 435–445 (2013). https://doi.org/10.1007/s12021-013-9196-z
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DOI: https://doi.org/10.1007/s12021-013-9196-z